fix: module 08 and module 09

42-AI · Jun 12, 2024 · e7deba7 · e7deba7
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diff --git a/module08/en.ex05_interlude.tex b/module08/en.ex05_interlude.tex
@@ -13,7 +13,7 @@ \subsection*{Vectorized Logistic Gradient}
 
 Given the previous logistic gradient formula, it's quite easy to produce a vectorized version.
 
-Actually, you almost already implemented it on module07!
+Actually, you almost already implemented it on module02!
 
 As with the previous exercise, \textbf{the only thing you have to change is your hypothesis} in order to calculate your logistic gradient.
 

diff --git a/module08/en.py_proj.tex b/module08/en.py_proj.tex
@@ -24,8 +24,7 @@ \chapter*{Common Instructions}
 
   \item Your manual is the internet.
 
-  \item You can also ask questions in the \texttt{\#bootcamps} channel in the \href{https://42-ai.slack.com}{42AI}
-  or \href{42born2code.slack.com}{42born2code}.
+  \item You can also ask questions on the {https://discord.gg/8Vvb6QMCZq}{42AI} discord.
 
   \item If you find any issue or mistakes in the subject please create an issue on \href{https://github.com/42-AI/bootcamp_python/issues}{42AI repository on Github}.  
 

diff --git a/module08/en.subject.tex b/module08/en.subject.tex
@@ -544,7 +544,7 @@ \chapter{Exercise 04}
 \turnindir{ex04}
 \exnumber{04}
 \exfiles{log\_gradient.py}
-\exforbidden{numpy and any function that performs derivatives for you}
+\exforbidden{any function that performs derivatives for you}
 \makeheaderfilesforbidden
 
 
@@ -819,7 +819,7 @@ \subsection*{Examples}
 # Example 1:
 mylr.loss_(X,Y)
 # Output:
-11.513157421577004
+11.513157421577002
 
 # Example 2:
 mylr.fit_(X, Y)

diff --git a/module08/usefull_ressources.tex b/module08/usefull_ressources.tex
@@ -15,26 +15,29 @@ \section*{Notions of the module}
 \section*{Useful Ressources}
 
 You are strongly advise to use the following resource:
-\href{https://www.coursera.org/learn/machine-learning/home/week/3}{Machine Learning MOOC - Stanford}
-Here are the sections of the MOOC that are relevant for today's exercises: 
+\href{https://www.coursera.org/learn/machine-learning}{Machine Learning MOOC - Stanford}
+These videos are available at no cost; simply log in, select "Enroll for Free", and choose "audit the course for free" in the popup window.
+The following sections of the course are pertinent to today's exercises:
 
-\subsection*{Week 3}
+\subsection*{Week 3: Classification}
 
-\subsubsection*{Classification and representation}
+\subsubsection*{Classification with logistic regression}
 \begin{itemize}
-  \item Classification (Video + Reading)
-  \item Hypothesis Representation (Video + Reading)
-  \item Decision Boundary (Video + Reading)
+  \item Motivations
+  \item Logistic regression
+  \item Decision boundary
 \end{itemize}
 
-\subsubsection*{Logistic Regression Model}
+\subsubsection*{Cost function for logistic regression}
 \begin{itemize}
-  \item Cost Function (Video + Reading)
-  \item Simplified Cost Function and Gradient Descent (Video + Reading)
+  \item Cost function for logistic regression
+  \item Simplified Cost Function for Logistic Regression
 \end{itemize}
 
-\subsubsection*{Multiclass Classification}
+\subsubsection*{Gradient descent for logistic regression}
 \begin{itemize}
-  \item Mutliclass Classification: One-vs-all (Video + Reading)
-  \item Review (Reading + Quiz)
+  \item Gradient Descent Implementation
 \end{itemize}
+
+
+\emph{All videos above are available also on this \href{https://youtube.com/playlist?list=PLkDaE6sCZn6FNC6YRfRQc_FbeQrF8BwGI&feature=shared}{Andrew Ng's YouTube playlist}, videos from \#31 to \#36.}
diff --git a/module09/en.py_proj.tex b/module09/en.py_proj.tex
@@ -24,8 +24,7 @@ \chapter*{Common Instructions}
 
   \item Your manual is the internet.
 
-  \item You can also ask questions in the \texttt{\#bootcamps} channel in the \href{https://42-ai.slack.com}{42AI}
-  or \href{42born2code.slack.com}{42born2code}.
+  \item You can also ask questions on the {https://discord.gg/8Vvb6QMCZq}{42AI} discord.
 
   \item If you find any issue or mistakes in the subject please create an issue on \href{https://github.com/42-AI/bootcamp_python/issues}{42AI repository on Github}.  
 

diff --git a/module09/en.subject.tex b/module09/en.subject.tex
@@ -1060,12 +1060,12 @@ \section*{Instructions}
 	"""
   supported_penalities = ['l2'] # We consider l2 penality only. One may wants to implement other penalities
 
-	def __init__(self, theta, alpha=0.001, max_iter=1000, penalty='l2', lambda_=1.0):
+	def __init__(self, theta, alpha=0.001, max_iter=1000, penality='l2', lambda_=1.0):
 		# Check on type, data type, value ... if necessary
     self.alpha = alpha
 		self.max_iter = max_iter
 		self.theta = theta
-		self.penalty = penalty
+		self.penality = penality
     self.lambda_ = lambda_ if penality in self.supported_penalities else 0
 		#... Your code ...
 
@@ -1075,8 +1075,8 @@ \section*{Instructions}
 \begin{itemize}
   \item \textbf{update} the \texttt{fit\_(self, x, y)} method: 
   \begin{itemize}
-    \item \texttt{if penalty == 'l2'}: use a \textbf{regularized version} of the gradient descent.
-    \item \texttt{if penalty = 'none'}: use the \textbf{unregularized version} of the gradient descent from \texttt{module03}.
+    \item \texttt{if penality == 'l2'}: use a \textbf{regularized version} of the gradient descent.
+    \item \texttt{if penality = 'none'}: use the \textbf{unregularized version} of the gradient descent from \texttt{module03}.
   \end{itemize}
 \end{itemize}
 

diff --git a/module09/usefull_ressources.tex b/module09/usefull_ressources.tex
@@ -15,26 +15,38 @@ \section*{Notions of the module}
 \section*{Useful Ressources}
 
 You are strongly advise to use the following resource:
-\href{https://www.coursera.org/learn/machine-learning/home/week/3}{Machine Learning MOOC - Stanford}
-Here are the sections of the MOOC that are relevant for today's exercises: 
+\href{https://www.coursera.org/learn/machine-learning}{Machine Learning MOOC - Stanford}
+These videos are available at no cost; simply log in, select "Enroll for Free", and choose "audit the course for free" in the popup window.
+The following sections of the course are pertinent to today's exercises:
 
-\subsection*{Week 3}
+\subsection*{Week 3: Classification}
 
-\subsubsection*{Classification and Representation}
+\subsubsection*{Classification with logistic regression (already seen on module 03)}
 \begin{itemize}
-  \item Classification (Video + Reading)
-  \item Hypothesis Representation (Video + Reading)
-  \item Decision Boundary (Video + Reading)
+  \item Motivations
+  \item Logistic regression
+  \item Decision boundary
 \end{itemize}
 
-\subsubsection*{Logistic Regression Model}
+\subsubsection*{Cost function for logistic regression (already seen on module 03)}
 \begin{itemize}
-  \item Cost Function (Video + Reading)
-  \item Simplified Cost Function and Gradient Descent (Video + Reading)
+  \item Cost function for logistic regression
+  \item Simplified Cost Function for Logistic Regression
 \end{itemize}
 
-\subsubsection*{Multiclass Classification}
+\subsubsection*{Gradient descent for logistic regression (already seen on module 03)}
 \begin{itemize}
-  \item Mutliclass Classification: One-vs-all (Video + Reading)
-  \item Review (Reading + Quiz)
+  \item Gradient Descent Implementation
 \end{itemize}
+
+\subsubsection*{The problem of overfitting (New !!!)}
+\begin{itemize}
+  \item The problem of overfitting
+  \item Addressing overfitting
+  \item Cost function with regularization
+  \item Regularized linear regression
+  \item Regularized logistic regression  
+\end{itemize}
+
+
+\emph{All videos above are available also on this \href{https://youtube.com/playlist?list=PLkDaE6sCZn6FNC6YRfRQc_FbeQrF8BwGI&feature=shared}{Andrew Ng's YouTube playlist}, videos from \#31 to \#36 (already seen on module 03) and \#37 to \#41 (new !!!).}